scholarly journals Nuclear DNA Content of Fine Needle Aspirates of Invasive Ductal Carcinomas of the Breast

1997 ◽  
Vol 13 (1) ◽  
pp. 1-8 ◽  
Author(s):  
Sudip Sarker ◽  
Allan Spigelman ◽  
Marjorie Walker ◽  
Dulcie Coleman
Keyword(s):  
Breast Cancer ◽  
Dna Content ◽  
Nuclear Dna ◽  
Histological Grade ◽  
Tumour Size ◽  
Nuclear Dna Content ◽  
Biological Assessment ◽  
Breast Cancers ◽  
Fine Needle ◽  
Fine Needle Aspirates

Patients with aggressive breast cancers benefit from chemotherapy prior to surgery. If the biology of the breast cancers were better characterised pre‐operatively, more patients at risk could be offered chemotherapy. We have assessed nuclear DNA content of fine needle aspirates (FNA) of 103 invasive ductal breast cancers and compared this to tumour size, node status and histological grade. Median follow‐up was 18 months so no prognostic studies were made. Diploid and non‐diploid tumours were distributed equally in node negative and positive patients. However non‐diploidy status increased in line with known prognostic markers of tumour size and histological grade. This suggests that ploidy might contribute to the pre‐operative assessment of prognosis. We conclude that nuclear DNA of breast cancer FNAs may be of value in the pre‐operative biological assessment of breast cancer patients.

scholarly journals Quantitative nuclear DNA content in fine needle aspirates of pancreatic cancer.

Gut ◽  
1991 ◽  
Vol 32 (3) ◽  
pp. 325-328 ◽  
Author(s):  
A R Weger ◽  
K S Glaser ◽  
G Schwab ◽  
D Oefner ◽  
E Bodner ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
Dna Content ◽  
Nuclear Dna ◽  
Nuclear Dna Content ◽  
Fine Needle ◽  
Fine Needle Aspirates

scholarly journals Cancer Grade Model: a multi-gene machine learning-based risk classification for improving prognosis in breast cancer

2021 ◽  
Author(s):  
E. Amiri Souri ◽  
A. Chenoweth ◽  
A. Cheung ◽  
S. N. Karagiannis ◽  
S. Tsoka
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Clinical Decision Making ◽  
Histological Grade ◽  
Tumour Size ◽  
Clinical Decision ◽  
Gene Signature ◽  
Risk Classification ◽  
Breast Cancers ◽  
Grade 3

Abstract Background Prognostic stratification of breast cancers remains a challenge to improve clinical decision making. We employ machine learning on breast cancer transcriptomics from multiple studies to link the expression of specific genes to histological grade and classify tumours into a more or less aggressive prognostic type. Materials and methods Microarray data of 5031 untreated breast tumours spanning 33 published datasets and corresponding clinical data were integrated. A machine learning model based on gradient boosted trees was trained on histological grade-1 and grade-3 samples. The resulting predictive model (Cancer Grade Model, CGM) was applied on samples of grade-2 and unknown-grade (3029) for prognostic risk classification. Results A 70-gene signature for assessing clinical risk was identified and was shown to be 90% accurate when tested on known histological-grade samples. The predictive framework was validated through survival analysis and showed robust prognostic performance. CGM was cross-referenced with existing genomic tests and demonstrated the competitive predictive power of tumour risk. Conclusions CGM is able to classify tumours into better-defined prognostic categories without employing information on tumour size, stage, or subgroups. The model offers means to improve prognosis and support the clinical decision and precision treatments, thereby potentially contributing to preventing underdiagnosis of high-risk tumours and minimising over-treatment of low-risk disease.

Prognostic value of nuclear DNA content in breast cancer in relation to tumor size, nodal status, and estrogen receptor content

1989 ◽  
Vol 13 (1) ◽  
pp. 23-32 ◽  
Author(s):  
Anette von Rosen ◽  
Lars Erik Rutqvist ◽  
John Carstensen ◽  
Anders Fallenius ◽  
Lambert Skoog ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Estrogen Receptor ◽  
Dna Content ◽  
Tumor Size ◽  
Prognostic Value ◽  
Nuclear Dna ◽  
Nuclear Dna Content ◽  
Nodal Status

Nuclear DNA Content as a Prognostic Factor in T1−2N0 Breast Cancer

1990 ◽  
Vol 93 (4) ◽  
pp. 471-479 ◽  
Author(s):  
Sakari Toikkanen ◽  
Heikki Joensuu ◽  
Pekka Klemi
Keyword(s):  
Breast Cancer ◽  
Prognostic Factor ◽  
Dna Content ◽  
Nuclear Dna ◽  
Nuclear Dna Content

Nuclear DNA content and prognosis in human breast cancer: A static cytophotometric study

1987 ◽  
Vol 9 (2) ◽  
pp. 101-109 ◽  
Author(s):  
Jennet Harvey ◽  
Nicholas de Klerk ◽  
Ian Berryman ◽  
Gregory Sterrett ◽  
Michael Byrne ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Dna Content ◽  
Human Breast Cancer ◽  
Nuclear Dna ◽  
Nuclear Dna Content ◽  
Human Breast ◽  
Cytophotometric Study

scholarly journals NUCLEAR MORPHOMETRY IN AFRICAN BREAST CANCER

2011 ◽  
Vol 21 (2) ◽  
pp. 145 ◽  
Author(s):  
Offiong Francis Ikpatt ◽  
Teijo Kuopio ◽  
Yrjö Collan
Keyword(s):  
Breast Cancer ◽  
Histological Grade ◽  
Tumour Size ◽  
Continuous Variable ◽  
Grading System ◽  
Breast Cancers ◽  
Nuclear Area ◽  
Cut Point ◽  
Nuclear Morphometry ◽  
Cox's Regression

Three hundred cases of invasive breast cancer diagnosed between 1983 and 1999 in Calabar, Nigeria were analysed to determine the nuclear morphometric variables, and evaluate the prognostic potential of nuclear morphometry in Nigerian breast cancers. The necessary follow-up was available for 129 patients. The nuclear area was the most valuable variable. In the Nigerian material, the mean nuclear area (MNA) (SD) was 89.2 (34.0) μm2. MNA was significantly higher in tumours of the postmenopausal than premenopausal (p = 0.0405), in LN+ than LN- (p = 0.0202) patients, and in tumours over 3 cm than smaller ones (p < 0.0001). There were also significant differences between different clinical stages, histological grades, and histological types of tumours. Significant correlations were observed between MNA and histological grade (r = 0.64), standard mitotic index (r = 0.45) and tumour size (r = 0.20). MNA as a continuous variable was a statistically significant prognosticator in the whole material (p = 0.0281), and among the postmenopausal patients (p = 0.0238). Univariate cox's regression demonstrated one significant grading cutpoint at MNA = 111 μm2, which divided the material into two groups of different survival. The development of a morphometric grading system optimal for the Nigerian material could use the latter cut-point between nuclear scores 2 and 3 in the grading system. The earlier proven cut-point of 47 μm2 could be used between nuclear scores 1 and 2.

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